code: def uncertain_predict_f_monte_carlo(
      self, Xmu: tf.Tensor, Xchol: tf.Tensor, mc_iter: int = int(1e6)
  ) -> MeanAndVariance:
      D_in = Xchol.shape[0]
      X_samples = Xmu + np.reshape(
          Xchol[None, :, :] @ rng.randn(mc_iter, D_in)[:, :, None],
          [mc_iter, D_in],
      )
      F_mu, F_var = self.predict_f(X_samples)
      F_samples = (
          F_mu + rng.randn(*F_var.shape) * (F_var**0.5)
      ).numpy()
      mean = np.mean(F_samples, axis=0)
      covar = np.cov(F_samples.T)
      return mean, covar
  
imports: from typing import Collection, Optional
  import numpy
  import pytest
  import tensorflow
  from check_shapes import ShapeChecker, check_shapes
  from numpy.testing import assert_allclose
  import gpflow
  from gpflow.base import AnyNDArray, MeanAndVariance
  from gpflow.conditionals import conditional, uncertain_conditional
  from gpflow.config import default_float
  from gpflow.mean_functions import Constant, Linear, MeanFunction, Zero
  from gpflow.quadrature import mvnquad
  from gpflow.utilities import training_loop
label_desc: Use image node_id 2 for calling the MomentMatchingSVGP obj's underlying member method code with example usage: obj.uncertain_predict_f_monte_carlo(Xmu, Xchol, mc_iter) and returns: mean,  covar